remains a challenge, especially the perception of ocean waves motion. [1][2][3][4] Ocean wave sensing devices based on additional complex mechanical and hydraulic structures typically transform the multidirectional wave motion into linear reciprocation or rotation of these structures to generate electric signals. However, some such devices are restrained by several drawbacks, including lower accuracy and robustness and expensive maintenance costs. Furthermore, impeller-type sensors increase the risk of collision between marine animals, [5] remote radar sensors impact the lives of marine animals due to the generation of a strong electromagnetic field, [6] and the laying of large sensing equipment generates noises and disturbances to marine animals [7] and changes hydrodynamic conditions. Conventional signal processing strategies have been demonstrated to possibly provide insufficient information to estimate ocean wave cues, resulting in difficulty to meet the requirements of high accuracy (error range in the order of 1 m). [8,9] Moreover, most of these ocean wave sensors need an external power supply, and the high cost of power supply limits their development. Therefore, novel ocean wave sensing techniques are still an open research topic.The design of efficient ocean wave sensors for monitoring the marine environment and revealing dynamic changes has been a major challenge. In this study, a self-powered bionic coral wave sensor (BCWS) based on a triboelectric nanogenerator is proposed. The BCWS captures wave data, which are useful for marine engineering construction, marine resource development, and marine disaster warning. It is mainly composed of triboelectric perceiving units (60 mm in length, 10 mm in width, and 1.5 mm in thickness) encapsulated in coral tentacles, a fixation mechanism, a buoyancy tray, and a counterweight mechanism. With the help of its bio-inspired structural design, the BCWS effectively improves the signal response time and sensitivity in the 3D perception of wave information. In particular, the coral tentacles stimulated by a load cause contact-separation between fluorinated ethylene propylene and conductive ink electrodes, thereby generating electric signals. This analysis of the experimental data reveals that the BCWS perceives wave height, wave frequency, wave period, and wave direction with millimeter accuracy. To demonstrate the applicability and stability of the BCWS, several of its potential functions are illustrated, including controlling light emitting diodes, perceiving wave information in the ocean, and assisting overboard rescue. The results show that the BCWS provides an intelligent solution for modern marine monitoring.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/admt.202101098.
Past research has studied offline proximity such as co-location and online social connections such as friendship individually. People form social relationships based on certain characteristics they possess, called social selection. When people change their social behavior due to interaction with others, social influence is at work. However, few researchers have examined the relationship that exists between offline proximity and online social connection, and the transitions from offline to online and vice versa (O2O).To study this problem, we created a system for finding and connecting with people at a conference that uses offline proximity encounters in order to help attendees meet and connect with each other. Using data where our system was deployed at two conferences, we discover that for social selection, more proximity interactions will result in an increased probability for a person to add another as a social connection (friend, follower or exchanged contact). However, after the social connections are established, more online social interactions result in a decreased duration and frequency of offline interactions between the connected users and social influence is weak. These results are just the first step in understanding how O2O interactions can help link people together, improve friend recommendations, and improve overall user experience.
Power system facility calibration is a compulsory task that requires in-site operations. In this work, we propose a remote calibration device that incorporates edge intelligence so that the required calibration can be accomplished with little human intervention. Our device entails a wireless serial port module, a Bluetooth module, a video acquisition module, a text recognition module, and a message transmission module. First, the wireless serial port is used to communicate with edge node, the Bluetooth is used to search for nearby Bluetooth devices to obtain their state information and the video is used to monitor the calibration process in the calibration lab. Second, to improve the intelligence, we propose a smart meter reading method in our device that is based on artificial intelligence to obtain information about calibration meters. We use a mini camera to capture images of calibration meters, then we adopt the Efficient and Accurate Scene Text Detector (EAST) to complete text detection, finally we built the Convolutional Recurrent Neural Network (CRNN) to complete the recognition of the meter data. Finally, the message transmission module is used to transmit the recognized data to the database through Extensible Messaging and Presence Protocol (XMPP). Our device solves the problem that some calibration meters cannot return information, thereby improving the remote calibration intelligence.
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